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概要と使いどころ
Announcement board: author, category, department, priority, audience, publish and expiry dates, status and acknowledgements, as CSV, SQL, JSON Schema or Notion on request. Use for internal notices.
日本語の概要は準備中です。原文の説明を表示しています。
Alumni and re-hire register: former role, last working day, re-hire eligibility, current employer and re-engagement date, as CSV, SQL, JSON Schema or Notion on request. Use for alumni outreach.
日本語の概要は準備中です。原文の説明を表示しています。
4-layer recall procedure (durable → semantic → PostgreSQL → daily notes) before claiming "no context". Invoke when user explicitly asks "do you remember", "what did we discuss about X", "search history". NOT a general assistant — only fires when recall is the explicit task. Drop the prior "use proactively" rule. Triggers: "do you remember", "what did we discuss", "when did", "find that conversation about".
日本語の概要は準備中です。原文の説明を表示しています。
Search past WhatsApp/chat conversations stored in the audit log PostgreSQL database. Use when the user asks about past conversations, what was discussed, what someone said, finding a specific message, or referencing previous discussions. Also use to reply to or quote specific past messages.
日本語の概要は準備中です。原文の説明を表示しています。
Single-skill wrapper for the 4-layer recall (durable memory → semantic DB → PostgreSQL → daily notes). Use BEFORE saying "I don't have context". Triggers: "search memory", "what do we know about X", "deep recall", "I don't remember".
日本語の概要は準備中です。原文の説明を表示しています。
Dashboard metric register: metric, source module, formula, period, value, target, trend, owner and last-updated, as CSV, SQL, JSON Schema or Notion on request. Use for KPI dashboards.
日本語の概要は準備中です。原文の説明を表示しています。
Scores shortlisted accounting packages against 57 evidence-backed fields, emitted as CSV, SQL, JSON Schema or Notion on request. Use for choosing accounting software.
日本語の概要は準備中です。原文の説明を表示しています。
Access matrix of role-by-module permissions, with per-role scope, confidentiality level and SME tier, as CSV, SQL, JSON Schema or Notion on request. Use for access reviews.
日本語の概要は準備中です。原文の説明を表示しています。
360 feedback register: reviewer, subject, review cycle, visibility, due date and score, as CSV, SQL, JSON Schema or Notion on request. Use for 360 reviews or peer feedback cycles.
日本語の概要は準備中です。原文の説明を表示しています。
Admin account register: system, main and backup admin, seats, plan, 2FA, shared logins and access-review dates, as CSV, SQL, JSON Schema or Notion on request. Use for access reviews.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Cockroachdb Cloud — cockroachDB Cloud (Serverless & Dedicated) management via the ccloud CLI and CockroachDB Cloud API. Covers clusters, databases, SQL users, networking, backups, and metrics. Use when managing CockroachDB Cloud clusters or reviewing database health.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Gcp Idle Resources — detect unused and idle GCP resources that incur cost without providing value. Covers unattached Persistent Disks, unused external IPs, stopped VMs, idle Cloud NAT, idle load balancers, old snapshots, idle Cloud SQL instances, abandoned GKE node pools, unused VPC connectors, and orphaned Filestore instances. Includes estimated monthly waste per resource and anti-hallucination rules for safe detection.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze Compute Engine VM, Cloud SQL, Persistent Disk, and serverless (Cloud Functions/Cloud Run) utilization to identify right-sizing opportunities. Uses Cloud Monitoring metrics with anti-hallucination rules for E2 shared-core instances, sole-tenant nodes, preemptible/spot VMs, SUD eligibility, peak vs average analysis, CUD-aware savings, and estimated monthly savings calculations.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Aws Rds Deep — aWS RDS deep analysis covering Performance Insights, event subscriptions, proxy management, and global database status. Covers PI counter metrics, wait event analysis, top SQL queries, read replica lag, storage autoscaling, and Aurora-specific diagnostics.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Singlestore — singleStore (MemSQL) workspace management, pipeline status, query tuning, memory analysis, and cluster health.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Tidb — tiDB cluster topology, slow query analysis, hot region analysis, dashboard monitoring, and SQL optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Cockroachdb — cockroachDB cluster health, range distribution, SQL statistics, schema change monitoring, and query optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.
日本語の概要は準備中です。原文の説明を表示しています。
AI-powered codebase security scanner that reasons about code like a security researcher — tracing data flows, understanding component interactions, and catching vulnerabilities that pattern-matching tools miss. Use this skill when asked to scan code for security vulnerabilities, find bugs, check for SQL injection, XSS, command injection, exposed API keys, hardcoded secrets, insecure dependencies, access control issues, or any request like "is my code secure?", "review for security issues", "audit this codebase", or "check for vulnerabilities". Covers injection flaws, authentication and access control bugs, secrets exposure, weak cryptography, insecure dependencies, and business logic issues across JavaScript, TypeScript, Python, Java, PHP, Go, Ruby, and Rust.
日本語の概要は準備中です。原文の説明を表示しています。
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
日本語の概要は準備中です。原文の説明を表示しています。
PHP 8.0+ development — XAMPP, RESTful APIs, PDO/MySQL/MariaDB, and authentication. Use when building PHP backends, creating API endpoints, configuring XAMPP, or integrating PHP with databases.
日本語の概要は準備中です。原文の説明を表示しています。
Zero-config Postgres for Netlify apps via @netlify/database — querying data from Functions/Edge Functions, writing schema migrations, setting up Drizzle ORM, local dev with netlify dev, database branches for deploy previews, and migrating an existing Postgres project onto Netlify. Use when adding a database, building a contact form or CRUD API, writing SQL migrations, wiring up Drizzle, running netlify database commands, testing with a local Postgres, or switching from Neon/Supabase/RDS to Netlify Database.
日本語の概要は準備中です。原文の説明を表示しています。
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
日本語の概要は準備中です。原文の説明を表示しています。
Expert en optimisation de bases de données (query plans, indexing strategies, partitioning, vacuum)
日本語の概要は準備中です。原文の説明を表示しています。